Scene luminance in an image can vary greatly over a vast range from bright sunlight to dark starlight. In order to capture the details in an image, a robust auto exposure control for image sensors is needed. The function of auto exposure (AE) is to allow an image sensor (e.g., camera) to automatically adapt to the surrounding illumination level and then help improve the overall system performance.
In recent years, several approaches for auto exposure control have been developed. The brightness of the output image is widely used as an important cue. Most auto exposure processes assume that the brightness of images and the exposure values of a camera have an approximately linear relation under variable background luminance (see the List of Incorporated Cited Literature References, Literature Reference Nos. 5 and 6). Unfortunately, this is certainly not true for a nonlinear image sensor. However, even for a linear image sensor, this assumption cannot hold since the brightness range of the camera is usually smaller than that of the scene. For example, the pixel value might be saturating, resulting in overexposure.
Additional approaches are based on the assumption that the exposure value corresponding to the median brightness provides the optimal value. These approaches either maintain the mean pixel-brightness of an image to a certain brightness value (see Literature Reference No. 7) or make as much of the image as possible appear in the middle region of a brightness histogram (see Literature Reference No. 8). However, it is difficult to obtain an appropriate image luminance under backlighting or excessive front lighting conditions where the luminance difference between the main object and the background is large. When the contrast of an object is high, a bright area is saturated and a dark area is masked.
An alternative strategy to address image brightness is to use image entropy, which is a measurement of image information content and has been used in many image processing applications. Some researchers have reported that image entropy can be used to measure the quantity of attention for a region of interest and, therefore, improve object detection (see Literature Reference Nos. 9 and 10). Existing entropy-based auto exposure control approaches assume that maximizing information entropy leads to maximal information content of images. Brightness entropy or RGB (red, green, blue) color entropy are used as image entropy (see Literature Reference Nos. 2, 3, and 4). Since optimal camera parameters are obtained by maximizing image content, these methods can work with any type of image sensor and robustly handle difficult light conditions. However, existing entropy-based processes may not focus on a region of interest, which may result in under-exposure and loss of information in the major interest area.
Thus, a continuing need exists for an auto exposure adjustment system that improves image quality and provides the ability to focus on a region of interest in an image.